Background Within the Dutch health care system the focus is shifting from a disease oriented appr... more Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP ...
Sutch; Predictive Models for health services utlization: Current and Future development Whilst th... more Sutch; Predictive Models for health services utlization: Current and Future development Whilst the emphasis of work has been on identifying the highest risk individuals, there is an increased interest in recognising earlier and emerging risk, where more preventative methods can be informed such as chronic disease self-management programs. These models in their current form are being used to identify populations, but work on newly emerging data from Electronic Health Records (EHR), Personal Health Records (PHR), and Social Care data is expected to provide greater insight into these populations and those with highest need. Other models are also focused on other types of utilization across the health care sytem such as emergency care, outpatient, and primary care visits.
OBJECTIVES To produce an efficient and practically implementable method, based on primary care da... more OBJECTIVES To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health domains and are experiencing a mismatch of care. The Johns Hopkins ACG System was explored as a tool for identification, using its Aggregated Diagnosis Group (ADG) categories. STUDY DESIGN Retrospective cross-sectional study using general practitioners' electronic health records combined with hospital data. METHODS A prediction model for patients with complex care needs was developed using a primary care population of 105,345 individuals. Dependent variables in the model included age, sex, and the 32 ADGs. The prediction model was externally validated on 30,793 primary care patients. Discrimination and calibrations were assessed by computing C statistics and by visual inspection of the calibration plot, respectively. RESULTS Our model was able to discriminate very well between complex and noncomplex patients (C statistic = 0.9; 95% CI, 0.88-0.92), whereas the calibration plot suggests that the model provides overestimates of complex patients. CONCLUSIONS With this study, the ACG System has proven to be a useful tool in the identification of patients with complex care needs in primary care, opening up possibilities for tailored interventions of care management for this complex group of patients. Utilizing ADGs, the prediction model that we developed had a very good discriminatory ability to identify those complex patients. However, the calibrating ability of the model still needs improvement.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-... more This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Additional file 2. Extended overview of individuals from each ACG category, distributed over 10 y... more Additional file 2. Extended overview of individuals from each ACG category, distributed over 10 year age bands.
systems (morbidity and pharmacy) to build whole population databases. Current projects include th... more systems (morbidity and pharmacy) to build whole population databases. Current projects include the development of measures for care management participation in US care plans, development of predictive models to assist English family doctors in identifying patients in the population needing outreach services, and applying the ACG System to other primary care populations in Europe and Australia.
Background Within the Dutch health care system the focus is shifting from a disease oriented appr... more Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP ...
A number of models are available in the United States (US) and the United Kingdom (UK) for use in... more A number of models are available in the United States (US) and the United Kingdom (UK) for use in predicting the risk of hospitalization, from general and insured populations. These models are being used in order to respond to health policies, such as Pay for Performance measures, aimed at reducing unnecessary hospital admissions, and to help patients avoid hospital admissions that are expensive and create risks to patient safety. These predictive models are being used for a variety of purposes including: screening patients for Case Management Programs and/or Disease Management Programs, organizational profiling, and assessing financial risk.
ObjectivePersistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-e... more ObjectivePersistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data.DesignA cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined.SettingCoded electronic health record data were extracted from 76 general practices in the Netherlands.ParticipantsPatients who were registered for at least 1 year during 2014–2018, were included (n=169 138).Outcome measuresIdentification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic c...
International Journal of Chronic Obstructive Pulmonary Disease
Multi-morbidity contributes to mortality and hospitalisation in COPD, but it is uncertain how thi... more Multi-morbidity contributes to mortality and hospitalisation in COPD, but it is uncertain how this interacts with disease severity in risk prediction. We compared contributions of multi-morbidity and disease severity factors in modelling future health risk using UK primary care healthcare data. Methods: Health records from 103,955 patients with COPD identified from the Clinical Practice Research Datalink were analysed. We compared area under the curve (AUC) statistics for logistic regression (LR) models incorporating disease indices with models incorporating categorised comorbidities. We also compared these models with performance of The John Hopkins Adjusted Clinical Groups ® System (ACG) risk prediction algorithm. Results: LR models predicting all-cause mortality outperformed models predicting hospitalisation. Mortality was best predicted by disease severity (AUC & 95% CI: 0.816 (0.805-0.827)) and prediction was enhanced only marginally by the addition of multi-morbidity indices (AUC & 95% CI: 0.829 (0.818-0.839)). The model combining disease severity and multi-morbidity indices was a better predictor of hospitalisation (AUC & 95% CI: 0.679 (0.672-0.686)). ACG-derived LR models outperformed conventional regression models for hospitalisation (AUC & 95% CI: 0.697 (0.690-0.704)) but not for mortality (AUC & 95% CI: 0.816 (0.805-0.827)). Conclusion: Stratification of future health risk in COPD can be undertaken using clinical and demographic data recorded in primary care, but the impact of disease severity and multimorbidity varies depending on the choice of health outcome. A more comprehensive risk modelling algorithm such as ACG offers enhanced prediction for hospitalisation by incorporating a wider range of coded diagnoses.
The objective of this research was to compare the casemix systems used in the United Kingdom (UK)... more The objective of this research was to compare the casemix systems used in the United Kingdom (UK), Australia and the United States of America (USA) to identify possible improvements in the design of the UK Healthcare Resource Groups.
This series of articles for rehabilitation in practice aims to cover a knowledge element of the r... more This series of articles for rehabilitation in practice aims to cover a knowledge element of the rehabilitation medicine curriculum. Nevertheless they are intended to be of interest to a multidisciplinary audience. The competency addressed in this article is 'An understanding of the different international models for funding of health care services and casemix systems, as exemplified by those in the US, Australia and the UK.'
Objective: To describe the rationale and development of a casemix model and costing methodology f... more Objective: To describe the rationale and development of a casemix model and costing methodology for tariff development for specialist neurorehabilitation services in the UK. Rationale for development of a new methodology: Patients with complex needs incur higher treatment costs. Fair payment should be weighted in proportion to costs of providing treatment, and should allow for variation over time Casemix model and band-weighting: Case complexity is measured by the Rehabilitation Complexity Scale (RCS). Cases are divided into five bands of complexity, based on the total RCS score. The principal determinant of costs in rehabilitation is staff time. Total staff hours/week (estimated from the Northwick Park Nursing and Therapy Dependency Scales) are analysed within each complexity band, through crosssectional analysis of parallel ratings. A 'band-weighting' factor is derived from the relative proportions of staff time within each of the five bands. Costing methodology: Total unit treatment costs are obtained from retrospective analysis of provider hospitals' budget and accounting statements. Mean bed-day costs (total unit cost/occupied bed days) are divided broadly into 'variable' and 'non-variable' components. In the weighted costing model, the bandweighting factor is applied to the variable portion of the bed-day cost to derive a banded cost, and thence a set of cost-multipliers. Preliminary data from one unit are presented to illustrate how this weighted costing model will be applied to derive a multilevel banded payment model, based on serial complexity ratings, to allow for change over time.
Objective To compare the utilisation of hospital beds in the NHS in England, Kaiser Permanente in... more Objective To compare the utilisation of hospital beds in the NHS in England, Kaiser Permanente in California, and the Medicare programme in the United States and California. Design Analysis of routinely available data from 2000 and 2001 on inpatient admissions, lengths of stay, and bed days in populations aged over 65 for 11 leading causes of use of acute beds. Setting Comparison of NHS data with data from Kaiser Permanente in California and the Medicare programme in California and the United States; interviews with Kaiser Permanente staff and visits to Kaiser facilities. Results Bed day use in the NHS for the 11 leading causes is three and a half times that of Kaiser's standardised rate, almost twice that of the Medicare California's standardised rate, and more than 50% higher than the standardised rate in Medicare in the United States. Kaiser achieves these results through a combination of low admission rates and relatively short stays. The lower use of bed days in Medicare in California compared with Medicare in the United States suggests there is a "California effect" as well as a "Kaiser effect" in hospital utilisation. Conclusion The NHS can learn from Kaiser's integrated approach, the focus on chronic diseases and their effective management, the emphasis placed on self care, the role of intermediate care, and the leadership provided by doctors in developing and supporting this model of care.
Background Within the Dutch health care system the focus is shifting from a disease oriented appr... more Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP ...
Sutch; Predictive Models for health services utlization: Current and Future development Whilst th... more Sutch; Predictive Models for health services utlization: Current and Future development Whilst the emphasis of work has been on identifying the highest risk individuals, there is an increased interest in recognising earlier and emerging risk, where more preventative methods can be informed such as chronic disease self-management programs. These models in their current form are being used to identify populations, but work on newly emerging data from Electronic Health Records (EHR), Personal Health Records (PHR), and Social Care data is expected to provide greater insight into these populations and those with highest need. Other models are also focused on other types of utilization across the health care sytem such as emergency care, outpatient, and primary care visits.
OBJECTIVES To produce an efficient and practically implementable method, based on primary care da... more OBJECTIVES To produce an efficient and practically implementable method, based on primary care data exclusively, to identify patients with complex care needs who have problems in several health domains and are experiencing a mismatch of care. The Johns Hopkins ACG System was explored as a tool for identification, using its Aggregated Diagnosis Group (ADG) categories. STUDY DESIGN Retrospective cross-sectional study using general practitioners' electronic health records combined with hospital data. METHODS A prediction model for patients with complex care needs was developed using a primary care population of 105,345 individuals. Dependent variables in the model included age, sex, and the 32 ADGs. The prediction model was externally validated on 30,793 primary care patients. Discrimination and calibrations were assessed by computing C statistics and by visual inspection of the calibration plot, respectively. RESULTS Our model was able to discriminate very well between complex and noncomplex patients (C statistic = 0.9; 95% CI, 0.88-0.92), whereas the calibration plot suggests that the model provides overestimates of complex patients. CONCLUSIONS With this study, the ACG System has proven to be a useful tool in the identification of patients with complex care needs in primary care, opening up possibilities for tailored interventions of care management for this complex group of patients. Utilizing ADGs, the prediction model that we developed had a very good discriminatory ability to identify those complex patients. However, the calibrating ability of the model still needs improvement.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-... more This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Additional file 2. Extended overview of individuals from each ACG category, distributed over 10 y... more Additional file 2. Extended overview of individuals from each ACG category, distributed over 10 year age bands.
systems (morbidity and pharmacy) to build whole population databases. Current projects include th... more systems (morbidity and pharmacy) to build whole population databases. Current projects include the development of measures for care management participation in US care plans, development of predictive models to assist English family doctors in identifying patients in the population needing outreach services, and applying the ACG System to other primary care populations in Europe and Australia.
Background Within the Dutch health care system the focus is shifting from a disease oriented appr... more Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP ...
A number of models are available in the United States (US) and the United Kingdom (UK) for use in... more A number of models are available in the United States (US) and the United Kingdom (UK) for use in predicting the risk of hospitalization, from general and insured populations. These models are being used in order to respond to health policies, such as Pay for Performance measures, aimed at reducing unnecessary hospital admissions, and to help patients avoid hospital admissions that are expensive and create risks to patient safety. These predictive models are being used for a variety of purposes including: screening patients for Case Management Programs and/or Disease Management Programs, organizational profiling, and assessing financial risk.
ObjectivePersistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-e... more ObjectivePersistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data.DesignA cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined.SettingCoded electronic health record data were extracted from 76 general practices in the Netherlands.ParticipantsPatients who were registered for at least 1 year during 2014–2018, were included (n=169 138).Outcome measuresIdentification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic c...
International Journal of Chronic Obstructive Pulmonary Disease
Multi-morbidity contributes to mortality and hospitalisation in COPD, but it is uncertain how thi... more Multi-morbidity contributes to mortality and hospitalisation in COPD, but it is uncertain how this interacts with disease severity in risk prediction. We compared contributions of multi-morbidity and disease severity factors in modelling future health risk using UK primary care healthcare data. Methods: Health records from 103,955 patients with COPD identified from the Clinical Practice Research Datalink were analysed. We compared area under the curve (AUC) statistics for logistic regression (LR) models incorporating disease indices with models incorporating categorised comorbidities. We also compared these models with performance of The John Hopkins Adjusted Clinical Groups ® System (ACG) risk prediction algorithm. Results: LR models predicting all-cause mortality outperformed models predicting hospitalisation. Mortality was best predicted by disease severity (AUC & 95% CI: 0.816 (0.805-0.827)) and prediction was enhanced only marginally by the addition of multi-morbidity indices (AUC & 95% CI: 0.829 (0.818-0.839)). The model combining disease severity and multi-morbidity indices was a better predictor of hospitalisation (AUC & 95% CI: 0.679 (0.672-0.686)). ACG-derived LR models outperformed conventional regression models for hospitalisation (AUC & 95% CI: 0.697 (0.690-0.704)) but not for mortality (AUC & 95% CI: 0.816 (0.805-0.827)). Conclusion: Stratification of future health risk in COPD can be undertaken using clinical and demographic data recorded in primary care, but the impact of disease severity and multimorbidity varies depending on the choice of health outcome. A more comprehensive risk modelling algorithm such as ACG offers enhanced prediction for hospitalisation by incorporating a wider range of coded diagnoses.
The objective of this research was to compare the casemix systems used in the United Kingdom (UK)... more The objective of this research was to compare the casemix systems used in the United Kingdom (UK), Australia and the United States of America (USA) to identify possible improvements in the design of the UK Healthcare Resource Groups.
This series of articles for rehabilitation in practice aims to cover a knowledge element of the r... more This series of articles for rehabilitation in practice aims to cover a knowledge element of the rehabilitation medicine curriculum. Nevertheless they are intended to be of interest to a multidisciplinary audience. The competency addressed in this article is 'An understanding of the different international models for funding of health care services and casemix systems, as exemplified by those in the US, Australia and the UK.'
Objective: To describe the rationale and development of a casemix model and costing methodology f... more Objective: To describe the rationale and development of a casemix model and costing methodology for tariff development for specialist neurorehabilitation services in the UK. Rationale for development of a new methodology: Patients with complex needs incur higher treatment costs. Fair payment should be weighted in proportion to costs of providing treatment, and should allow for variation over time Casemix model and band-weighting: Case complexity is measured by the Rehabilitation Complexity Scale (RCS). Cases are divided into five bands of complexity, based on the total RCS score. The principal determinant of costs in rehabilitation is staff time. Total staff hours/week (estimated from the Northwick Park Nursing and Therapy Dependency Scales) are analysed within each complexity band, through crosssectional analysis of parallel ratings. A 'band-weighting' factor is derived from the relative proportions of staff time within each of the five bands. Costing methodology: Total unit treatment costs are obtained from retrospective analysis of provider hospitals' budget and accounting statements. Mean bed-day costs (total unit cost/occupied bed days) are divided broadly into 'variable' and 'non-variable' components. In the weighted costing model, the bandweighting factor is applied to the variable portion of the bed-day cost to derive a banded cost, and thence a set of cost-multipliers. Preliminary data from one unit are presented to illustrate how this weighted costing model will be applied to derive a multilevel banded payment model, based on serial complexity ratings, to allow for change over time.
Objective To compare the utilisation of hospital beds in the NHS in England, Kaiser Permanente in... more Objective To compare the utilisation of hospital beds in the NHS in England, Kaiser Permanente in California, and the Medicare programme in the United States and California. Design Analysis of routinely available data from 2000 and 2001 on inpatient admissions, lengths of stay, and bed days in populations aged over 65 for 11 leading causes of use of acute beds. Setting Comparison of NHS data with data from Kaiser Permanente in California and the Medicare programme in California and the United States; interviews with Kaiser Permanente staff and visits to Kaiser facilities. Results Bed day use in the NHS for the 11 leading causes is three and a half times that of Kaiser's standardised rate, almost twice that of the Medicare California's standardised rate, and more than 50% higher than the standardised rate in Medicare in the United States. Kaiser achieves these results through a combination of low admission rates and relatively short stays. The lower use of bed days in Medicare in California compared with Medicare in the United States suggests there is a "California effect" as well as a "Kaiser effect" in hospital utilisation. Conclusion The NHS can learn from Kaiser's integrated approach, the focus on chronic diseases and their effective management, the emphasis placed on self care, the role of intermediate care, and the leadership provided by doctors in developing and supporting this model of care.
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